Conventional magnetic resonance imaging (“MRI”) pulse sequences include repetitive similar preparation phases, waiting phases, and acquisition phases that serially produce signals from which images can be made. The preparation phase determines when a signal can be acquired and determines the properties of the acquired signal. For example, a first pulse sequence may produce a T1-weighted signal at a first echo time (“TE”), while a second pulse sequence may produce a T2-weighted signal at a second TE. These conventional pulse sequences typically provide qualitative results where data are acquired with various weightings or contrasts that highlight a particular parameter (e.g., T1 relaxation, T2 relaxation).
When magnetic resonance (“MW”) images are generated, they may be viewed by a radiologist and/or surgeon who interprets the qualitative images for specific disease signatures. The radiologist may examine multiple image types (e.g., T1-weighted, T2-weighted) acquired in multiple imaging planes to make a diagnosis. The radiologist or other individual examining the qualitative images may need particular skill to be able to assess changes from session to session, from machine to machine, and from machine configuration to machine configuration.
Magnetic resonance fingerprinting (“MRF”) is a technology, which is described, as one example, by D. Ma, et al., in “Magnetic Resonance Fingerprinting,” Nature, 2013; 495(7440):187-192, that allows one to characterize tissue species using nuclear magnetic resonance (“NMR”). MRF can identify different properties of a resonant species (e.g., T1 spin-lattice relaxation, T2 spin-spin relaxation, proton density) to thereby correlate this information to quantitatively assess tissue properties. Other properties like tissue types and super-position of attributes can also be identified using MRF. These properties and others may be identified simultaneously using MRF.
In particular, unlike conventional MRI, MRF employs a series of varied sequence blocks (e.g., variable acquisition parameters) to gather tissue information based on unique signal evolutions generated in different resonant species (e.g., tissues) to which a radio frequency (“RF”) is applied. The signals from different resonant tissues will, however, be different and can be distinguished using MRF techniques. The different signals can be collected over a period of time to identify a signal evolution within a voxel. Resonant species in the voxel can then be characterized by comparing the signal evolution to known evolutions, for example, by comparing acquired signal evolutions to known evolutions using a pattern matching algorithm. Characterizing the resonant species may include identifying a material and tissue type. Alternatively, characterizing the resonant species may include identifying MR parameters associated with the resonant species. The “known” evolutions may be, for example, simulated evolutions calculated from physical principles and/or previously acquired evolutions. A large set of known evolutions may be stored in a MRF dictionary.
One of the challenges associated with the MRF dictionary approach is the large amount of data that is generated. In particular instances, such as when fine MRF dictionaries are needed or multiple components are taken into account (i.e. chemical exchange effects), the number of elements in an MRF dictionary can approach billions. In these cases, the process of making, storing, loading, and processing MRF dictionaries can become difficult even when using modern computers.
Currently, there is a need in the art to reduce the memory requirement and improve the efficiency of producing and using MRF dictionaries.